Multi-resolution dynamical downscaling of CMIP6 GCMs across the Western United States using WRF4: Quantifying climate change uncertainty down to the convective scale

Abstract:

The successful pursuit of a convergent, physically-based climate change signal, especially on regional scales, is severely limited by the coarse meshes used in many state-of-the-art GCMs reporting to the 6^th Coupled Model Intercomparison Project (CMIP6). As an ever-increasing number of modeling groups report their respective GCM results, the exploration of the high-resolution climate change magnitude and uncertainty can be explored like never before via dynamical downscaling. Here we introduce an ongoing collaborative effort dedicated towards improving the quality and constraining the uncertainty of the climate change signal across the western United States (WUS) by dynamically downscaling CMIP6 GCMs using WRF version 4. Specifically, we downscale WRF driven by GCMs from numerous modeling groups subject to different emissions scenarios. For some cases, we are downscaling multiple realizations from the same GCM and emissions scenario, which together with the range of driving simulations will result in an unprecedented ability to explore a range of plausible regional climate outcomes. Our 9-km results will be used to drive convective-permitting 3-km experiments across two areas: the entire state of California and the other covering a large portion of the Northern Rockies. We intend this dataset to serve others who wish to dynamically downscale historical and ScenarioMIP GCMs to other high resolution meshes across the WUS, drive land surface, fire, or hydrological models, or otherwise study the climate change signal across their locale of interest.